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See It Work
SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+ SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+
Available CORE-RCA-FAIL-AGT-001 AI Agent

Agentic Root Cause Analysis Agent (Failure Investigator)

Autonomously investigates equipment failures using fault tree analysis, causal inference, and pattern recognition across multiple data types — delivering consistent, evidence-backed root cause findings and corrective action recommendations that prevent recurrence rather than just restoring uptime.

ManufacturingMiningOil & GasEnergy & UtilitiesWater & Wastewater Root Cause Analysis

Target outcome · Faster, more consistent failure investigations that identify true root causes and systemic patterns — reducing recurring failures, lowering maintenance costs, and embedding reliability intelligence into operations at scale.

Business problem

Industrial operations face a recurring failure cycle driven by incomplete root cause investigations. Traditional RCA methods stop at proximate causes, overlook cross-functional patterns, and rely heavily on individual investigator expertise and availability. Bias, inconsistency, and time pressure produce investigations that prioritise fast recovery over root cause accuracy — leaving systemic factors unaddressed and setting the stage for repeat failures.

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Similar failures across equipment or sites go undetected due to fragmented analysis that does not connect patterns across the fleet. Institutional memory is scattered across aging reports, informal conversations, and retiring experts — creating knowledge gaps that new investigators cannot fill. The result is a plateau in reliability performance where recurring failures consume time, capital, and trust while more advanced competitors leverage systematic, AI-driven reliability programmes to accelerate improvement.

What it does

The Root Cause Analysis Agent is an autonomous Decision Agent that investigates equipment failures with consistency and governed autonomy using Composite AI — combining fault tree logic, causal inference, hypothesis testing, pattern recognition, and failure mode decomposition.

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It analyses failure events across equipment fleets, correlates data across historians, CMMS, sensor systems, and operator notes, and delivers transparent investigation reports with traceable evidence paths, confidence levels, and alternative hypothesis documentation. The agent continuously learns from investigation outcomes and corrective action effectiveness to refine its analytical models.

Current process vs. with AI Agent

TODAY · ROOT CAUSE ANALYSISREACTIVE
×
Investigation initiationInvestigations triggered manually and queued behind higher-priority operational tasks; significant delay
×
Root cause identification depthInvestigations stop at proximate cause due to time pressure; systemic factors missed
×
Cross-fleet pattern detectionSimilar failure patterns across assets or sites go undetected without dedicated fleet-wide analysis
×
Corrective action qualityCorrective actions target symptoms rather than root causes; recurrence rates remain high

Outcomes and measurement

Investigation cycle time

Baseline Days to weeks for complex failure investigations; simple cases often deprioritised
With agent Significant reduction through automated structured analysis; initial findings within hours

Recurring failure rate

Baseline High recurrence due to symptom-only corrective actions
With agent Measurable reduction through root-cause-targeted interventions and systemic pattern elimination

Investigation consistency

Baseline Variable quality based on investigator expertise, availability, and cognitive bias
With agent Consistent, methodology-driven analysis applied uniformly across all failure events

Knowledge retention

Baseline Investigative expertise resident in individuals and at risk from workforce turnover
With agent Codified in agent decision models and continuously refined from outcome feedback

*All figures are typical ranges. Achievable range depends on existing control maturity, data quality, and site-specific conditions.

Data inputs

Other

Ingests structured and unstructured failure-related data via XMPro Data Stream Designermaintenance records and CMMS work ordersoperator notes and inspection resultsQA and QC reportsenvironmental data

including real-time telemetry

sensorsalarmscontrol systems

historical data from historians and SCADA

and engineering documentation including design specifications and failure mode libraries

*Categories only — no tag names or system-specific field references. Exact data mapping is scoped per site.

Scoping questions

Expect these questions in a first scoping conversation. They signal engineering discipline and help narrow the template to your specific site context.

  1. What failure event types and equipment categories are highest priority for automated investigation, and what data sources are available for each?
  2. Do you have a historical database of failure investigations and corrective actions that can be used to seed the agent's causal models and failure mode libraries?
  3. What governance process is required for escalating sensitive or high-consequence investigations to human reliability engineers for review and sign-off?
  4. How will the agent's investigation findings and corrective action recommendations integrate with your CMMS or corrective action management system?
  5. Are there cross-site or cross-fleet failure pattern analysis requirements, and does the agent need to operate across multiple facilities simultaneously?

Want our AI to walk you through these scoping questions?

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Get specialist advice on scoping this for your site.

Our specialists will help you understand how the Agentic Root Cause Analysis Agent (Failure Investigator) fits your operations, what data you'd need, and what a scoping engagement typically looks like.

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